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The decision-making to outsource and select the most suitable global manufacturing outsourcing partner (MOP) is complex and uncertain due to multiple conflicting qualitative and quantitative criteria as well as multiple alternatives. Vagueness and variability exist in ratings of criteria and alternatives by group of decision-makers (DMs). The paper provides a novel Stochastic Fuzzy (SF) method for evaluation and selection of agile and sustainable global MOP in uncertain and volatile business environment.

Four main selection criteria for global MOP selection were identified such as economic, agile, environmental and social criteria. Total 16 sub-criteria were selected. To consider the vagueness and variability in ratings by group of DMs, SF method using t-distribution or z-distribution was adopted. The criteria weights were determined using the Stochastic Fuzzy-CRiteria Importance Through Intercriteria Correlation (SF-CRITIC), while MOP selection was carried out using Stochastic Fuzzy-VIseKriterijumskaOptimizacija I KompromisnoResenje (SF-VIKOR) in the case study of footwear industry. Sensitivity analysis was performed to test the robustness of the proposed model. A comparative analysis of SF-VIKOR and VIKOR was made.

The worker’s wages and welfare, product price, product quality, green manufacturing process and collaboration with partners are the most important criteria for MOP selection. The MOP3 was found to be the best agile and sustainable global MOP for the footwear company. In sensitivity analysis, significance level is found to have important role in MOP ranking. Hence, the study concluded that integrated SF-CRITIC and SF-VIKOR is an improved method for MOP selection problem.

In a group decision-making, ambiguity, impreciseness and variability are found in relative ratings. Fuzzy variant Multi-Criteria Decision-Making methods cover impreciseness in ratings but not the variability. On the other hand, deterministic models do not cover either. Hence, the stochastic method based on the probability theory combining fuzzy theory is proposed to deal with decision-making problems in imprecise and uncertain environments. Most notably, the proposed model has novelty as it captures and reveals both the stochastic perspective and the fuzziness perspective in rating by group of DMs.

The proposed multi-criteria group decision-making model contributes to the sustainable and agile footwear supply chain management and will help the policymakers in selecting the best global MOP.

To the best of the authors’ knowledge, SF method has not been used to select MOP in the existing literature. For the first time, integrated SF-CRITIC and SF-VIKOR method were applied to select the best agile and sustainable MOP under uncertainty. Unlike other studies, this study considered agile criteria along with triple bottom line sustainable criteria for MOP selection. The novel method of SF assessment contributes to the literature and put forward the managerial implication for improving agility and sustainability of global manufacturing outsourcing in footwear industry.

Blood availability is critical for saving lives in various healthcare services. Ensuring blood availability can only be achieved through efficient management of the blood supply chain (BSC). A key component of the BSC is bloodmobiles, which are responsible for a significant portion of blood donation collections. The most crucial factor affecting the efficacy of bloodmobiles is their location selection. Therefore, detailed decision analyses are essential for the location selection of bloodmobiles. This study proposes a comprehensive approach to bloodmobile location selection for resilient BSCs.

This study provides a novel integration of the spherical fuzzy analytical hierarchy process (SF-AHP) and spherical fuzzy complex proportional assessment (SF-COPRAS) methodologies. In this framework, the criteria are weighted using SF-AHP. The alternatives are then evaluated using SF-COPRAS, employing criteria weights obtained from SF-AHP without defuzzification.

The results show that supply conditions and resilience are the most important criteria for a bloodmobile location selection. Additionally, the validation analyses confirm the stability of the solution.

This study presents several managerial implications that can aid mid-level managers in the BSC during the decision-making process for bloodmobile location selection. The critical factors revealed, along with their importance in choosing bloodmobile locations, serve as a comprehensive guide. Additionally, the framework proposed in this study offers decision-makers (DMs) an effective method for ranking potential bloodmobile locations.

This study presents the first application of multi-criteria decision-making (MCDM) for bloodmobile location selection. In this manner, several aspects of bloodmobile location selection are considered for the first time in the existing literature. Furthermore, from the methodological aspect, this study provides a novel SF-AHP-integrated SF-COPRAS methodology.

The main purpose of this study is to provide a comprehensive review and critical insights of the application of fuzzy methods in modeling, assessing and understanding the various aspects of green and sustainable supply chains (SSCs).

The present study conducts a systematic literature review (SLR) and bibliometric analysis of 252 research articles. This study employs various tools such as VOSviewer version 1.6.10, Publish or Perish, Mendeley and Excel that aid in descriptive analysis, bibliometric analysis and network visualization. These tools have been used for performing citation analysis, top authors' analysis, co-occurrence of keywords, cluster and content analysis.

The authors have divided the literature into seven application areas and discussed detailed insights. This study has observed that research in the social sustainability area, including various issues like health and safety, labor rights, discrimination, etc. is scarce. Integration of the Industry 4.0 technologies like blockchain, big data analytics, Internet of Things (IoT) with the sustainable and green supply chain (GSC) is a promising field for future research.

The authors' contribution primarily lies in providing the integrated framework which shows the changing trends in the use of fuzzy methods in the sustainability area classifying and consolidating green and sustainable supply chain management (SSCM) literature in seven major areas where fuzzy methods are predominantly applied. These areas have been obtained after the analysis of clusters and content analysis of the literature presenting key insights from the past and developing the conceptual framework for future research studies.

This study aims to provide a scientific framework for the selection of suitable substation technology in an electrical power distribution network.

The present paper focuses on adopting an integrated multi-criteria decision-making approach using the Delphi method, analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS). The AHP is used to ascertain the criteria weights, and the TOPSIS is used for choosing the most fitting technology among choices of air-insulated substation, gas-insulated substation (GIS) and hybrid substation, to guarantee educated and supported choice.

The results reveal that the GIS is the most preferred technology by area experts, considering all the criteria and their relative preferences.

The current research has implications for public and private organizations responsible for the management of electricity in India, particularly the distribution system as the choice of substations is an essential component that has a strong impact on the smooth functioning and performance of the energy distribution in the country. The implementation of the chosen technology not only reduces economic losses but also contributes to the reduction of power outages, minimization of energy losses and improvement of the reliability, security, stability and quality of supply of the electrical networks.

The study explores the impact of substation technology installation in terms of its economic and environmental challenges. It emphasizes the need for proper installation checks to avoid long-term environmental hazards. Further, it reports that the economic benefits should not come at the cost of ecological degradation.

The present study is the first to provide a decision support framework for the selection of substation technologies using the hybrid AHP-TOPSIS approach. It also provides a cost–benefit analysis with short-term and long-term horizons. It further pinpoints the environmental issues with the installation of substation technology.

Identifying the barriers that hinder the circularity in the wine industry in Bulgaria and proposing a preliminary circular economy (CE) index for ranking in order of importance the barriers for providing a suitable and replicable model through the contextualization of the study applied at the territorial level.

The article adopts a Fuzzy AHP methodology based on 6 main barriers and 19 sub-barriers to find the weights of the barriers and rank them. The study was conducted in Bulgaria, and it consists of a questionnaire with pairwise comparisons to ask for expert opinions of members of the Executive Board of the National Association of Bulgarian Vinegrowers and additional members of the regional vine and winegrowing chambers.

The study identifies training and education and environmental barriers as the most important among the main barriers whereas, economic barriers are assessed as low impact. Considering the sub-barriers, lack of consumer awareness and interest, unclear taxation for CE, lack of knowledge about environmental emissions (carbon, water etc.) within the supply chains, and lack of digital trends are determined as the most significant sub-barriers.

The preliminary CE index can be practically used and tested, when necessary, by each winemaker according to their understanding and opinion of CE barriers and sub-barriers. The ranking within the CE index can lead winemakers also to decisions related to the company's social policy.

The study uses the Fuzzy AHP methodology with expert opinions to analyze and weigh the main barriers to achieving CE at the micro-level.

The purpose of this study is to establish a hierarchy of critical success factors to develop a framework for evaluating the performance of smart grids from a sustainability perspective.

The fuzzy analytical hierarchy process is used in this study to assess and determine the relative weight of economic, operational and environmental criteria. At the same time, the evidential reasoning algorithm is used to determine the belief degree of expert’s opinion, and the expected utility theory for the crisp value of success factors in performance estimation.

The finding reveals that success factors associated with the economic criteria receive significantly more attention from the expert group. Sensitivity analysis indicates the ranking of consumer satisfaction remains stable no matter how criteria weights are changed, which verifies the robustness and effectiveness of the proposed model and evaluation results.

The study presents a solid mathematical framework for collaborative system modeling and systematic analysis. Managers and stakeholders may use the proposed technique as a flexible tool to improve the energy system’s resiliency in a systematic way.

The authors analysed the operations of two synchronised channels by focusing on “buy online and return in store” (BORS) strategies in fast-fashion retail by investigating internal and external factors affecting this omnichannel strategy.

The authors apply a combination of techniques to identify the BORS factors. Firstly, a strengths, weaknesses, opportunities and threats (SWOT) analysis was used to define the operational factors of BORS adoption. The authors then apply analytic hierarchy process (AHP) to evaluate the factors under four SWOT categories for kids, male and female consumer groups. The factors of BORS were then ranked using the fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje (fuzzy VIKOR) approach.

Combining the SWOT, AHP and fuzzy VIKOR techniques, the authors identified 21 factors in this study. The opportunity that BORS provides for trying in the fitting room for a better convenient shopping experience was ranked as the most important factor, followed by the opportunity to create a loyal customer profile with an easy and well-organised return process. Furthermore, the results reveal that the child consumer group is the most critical of the stated operations factors, followed by male and female consumers.

The authors described the operational factors and supported the decision-making system of BORS for each consumer group with a priority ranking to realise effective managerial management for fast-fashion retailers and practitioners.

The study contributes to the growing literature on the BORS omnichannel strategy, specifically for fast-fashion retail based on consumer needs.

This paper aims to present a literature review of the most recent optimisation methods applied to Credit Scoring Models (CSMs).

The research methodology employed technical procedures based on bibliographic and exploratory analyses. A traditional investigation was carried out using the Scopus, ScienceDirect and Web of Science databases. The papers selection and classification took place in three steps considering only studies in English language and published in electronic journals (from 2008 to 2022). The investigation led up to the selection of 46 publications (10 presenting literature reviews and 36 proposing CSMs).

The findings showed that CSMs are usually formulated using Financial Analysis, Machine Learning, Statistical Techniques, Operational Research and Data Mining Algorithms. The main databases used by the researchers were banks and the University of California, Irvine. The analyses identified 48 methods used by CSMs, the main ones being: Logistic Regression (13%), Naive Bayes (10%) and Artificial Neural Networks (7%). The authors conclude that advances in credit score studies will require new hybrid approaches capable of integrating Big Data and Deep Learning algorithms into CSMs. These algorithms should have practical issues considered consider practical issues for improving the level of adaptation and performance demanded for the CSMs.

The results of this study might provide considerable practical implications for the application of CSMs. As it was aimed to demonstrate the application of optimisation methods, it is highly considerable that legal and ethical issues should be better adapted to CSMs. It is also suggested improvement of studies focused on micro and small companies for sales in instalment plans and commercial credit through the improvement or new CSMs.

The economic reality surrounding credit granting has made risk management a complex decision-making issue increasingly supported by CSMs. Therefore, this paper satisfies an important gap in the literature to present an analysis of recent advances in optimisation methods applied to CSMs. The main contribution of this paper consists of presenting the evolution of the state of the art and future trends in studies aimed at proposing better CSMs.

Smart grid is an integration between traditional electricity grid and communication systems and networks. Providing reliable services and functions is a critical challenge for the success and diffusion of smart grids that needs to be addressed. The purpose of this study is to determine the critical criteria that affect smart grid reliability from the perspective of users and investigate the role big data plays in smart grid reliability.

This study presents a model to investigate and identify criteria that influence smart grid reliability from the perspective of users. The model consists of 12 sub-criteria covering big data management, communication system and system characteristics aspects. Multi-criteria decision-making approach is applied to analyze data and prioritize the criteria using the fuzzy analytic hierarchy process based on the triangular fuzzy numbers. Data was collected from 16 experts in the fields of smart grid and Internet of things.

The results show that the “Big Data Management” criterion has a significant impact on smart grid reliability followed by the “System Characteristics” criterion. The “Data Analytics” and the “Data Visualization” were ranked as the most influential sub-criteria on smart grid reliability. Moreover, sensitivity analysis has been applied to investigate the stability and robustness of results. The findings of this paper provide useful implications for academicians, engineers, policymakers and many other smart grid stakeholders.

The users are not expected to actively participate in smart grid and its services without understanding their perceptions on smart grid reliability. Very few works have studied smart grid reliability from the perspective of users. This study attempts to fill this considerable gap in literature by proposing a fuzzy model to prioritize smart grid reliability criteria.

The purpose of this paper is to provide scientific guidance for the integration of industrialization and information (TIOII). In recent years, TIOII has promoted the development of intelligent manufacturing in China. However, many enterprises blindly invest in TIOII, which affects their normal production and operation.

This study establishes an efficiency evaluation model for TIOII. In this paper, entropy analytic hierarchy process (AHP) constraint cone and cross-efficiency are added based on traditional data envelopment analysis (DEA) model, and entropy AHP–cross-efficiency DEA model is proposed. Then, statistical analysis is carried out on the integration efficiency of enterprises in Guangzhou using cross-sectional data, and the traditional DEA model and entropy AHP–cross-efficiency DEA model are used to analyze the integration efficiency of enterprises.

The data show that the efficiency of enterprise integration is at a medium level in Guangzhou. The efficiency of enterprise integration has no significant relationship with enterprise size and production type but has a low negative correlation with the development level of enterprise integration. In addition, the improved DEA model can better reflect the real integration efficiency of enterprises and obtain complete ranking results.

By adding the entropy AHP constraint cone and cross-efficiency, the traditional DEA model is improved. The improved DEA model can better reflect the real efficiency of TIOII and obtain complete ranking results.